As will be shown in the results section, the features extracted using the feature alignment trained with VAE are blurry, and this is due to the variational autoencoder nature [1]}. To improve the quality of the generated images, we couple a generator network \(G\)  and a discriminator network \(D\)  to the features. In this way, the feature acts as a second latent vector, with a more complex distribution sampled from the simpler one. This generator network is similar to the refiner network presented in Ref. [2]}, which take an image as input and outputs an improved version of it.
